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Speed Estimation using Ultralytics YOLO11 🚀

What is Speed Estimation?

Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using Ultralytics YOLO11 you can now calculate the speed of object using object tracking alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.



Watch: Speed Estimation using Ultralytics YOLO11

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For deeper insights into speed estimation, check out our blog post: Ultralytics YOLO11 for Speed Estimation in Computer Vision Projects

Advantages of Speed Estimation?

  • Efficient Traffic Control: Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.
  • Precise Autonomous Navigation: In autonomous systems like self-driving cars, reliable speed estimation ensures safe and accurate vehicle navigation.
  • Enhanced Surveillance Security: Speed estimation in surveillance analytics helps identify unusual behaviors or potential threats, improving the effectiveness of security measures.

Real World Applications

Transportation Transportation
Speed Estimation on Road using Ultralytics YOLO11 Speed Estimation on Bridge using Ultralytics YOLO11
Speed Estimation on Road using Ultralytics YOLO11 Speed Estimation on Bridge using Ultralytics YOLO11

Speed Estimation using YOLO11 Example

# Run a speed example
yolo solutions speed show=True

# Pass a source video
yolo solutions speed source="path/to/video/file.mp4"

# Pass region coordinates
yolo solutions speed region=[(20, 400), (1080, 400), (1080, 360), (20, 360)]
import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("Path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

# Video writer
video_writer = cv2.VideoWriter("speed_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Define speed region points
speed_region = [(20, 400), (1080, 400), (1080, 360), (20, 360)]

speed = solutions.SpeedEstimator(
    show=True,  # Display the output
    model="yolo11n-pose.pt",  # Path to the YOLO11 model file.
    region=speed_region,  # Pass region points
    # classes=[0, 2],  # If you want to estimate speed of specific classes.
    # line_width=2,  # Adjust the line width for bounding boxes and text display
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()

    if success:
        out = speed.estimate_speed(im0)
        video_writer.write(im0)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
        continue

    print("Video frame is empty or video processing has been successfully completed.")
    break

cap.release()
cv2.destroyAllWindows()
Speed is Estimate

Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary depending on GPU speed.

Arguments SpeedEstimator

Name Type Default Description
model str None Path to Ultralytics YOLO Model File
region list [(20, 400), (1260, 400)] List of points defining the counting region.
line_width int 2 Line thickness for bounding boxes.
show bool False Flag to control whether to display the video stream.

Arguments model.track

Argument Type Default Description
source str None Specifies the source directory for images or videos. Supports file paths and URLs.
persist bool False Enables persistent tracking of objects between frames, maintaining IDs across video sequences.
tracker str botsort.yaml Specifies the tracking algorithm to use, e.g., bytetrack.yaml or botsort.yaml.
conf float 0.3 Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
iou float 0.5 Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
classes list None Filters results by class index. For example, classes=[0, 2, 3] only tracks the specified classes.
verbose bool True Controls the display of tracking results, providing a visual output of tracked objects.

FAQ

How do I estimate object speed using Ultralytics YOLO11?

Estimating object speed with Ultralytics YOLO11 involves combining object detection and tracking techniques. First, you need to detect objects in each frame using the YOLO11 model. Then, track these objects across frames to calculate their movement over time. Finally, use the distance traveled by the object between frames and the frame rate to estimate its speed.

Example:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
video_writer = cv2.VideoWriter("speed_estimation.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Initialize SpeedEstimator
speed_obj = solutions.SpeedEstimator(
    region=[(0, 360), (1280, 360)],
    model="yolo11n.pt",
    show=True,
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        break
    im0 = speed_obj.estimate_speed(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

For more details, refer to our official blog post.

What are the benefits of using Ultralytics YOLO11 for speed estimation in traffic management?

Using Ultralytics YOLO11 for speed estimation offers significant advantages in traffic management:

  • Enhanced Safety: Accurately estimate vehicle speeds to detect over-speeding and improve road safety.
  • Real-Time Monitoring: Benefit from YOLO11's real-time object detection capability to monitor traffic flow and congestion effectively.
  • Scalability: Deploy the model on various hardware setups, from edge devices to servers, ensuring flexible and scalable solutions for large-scale implementations.

For more applications, see advantages of speed estimation.

Can YOLO11 be integrated with other AI frameworks like TensorFlow or PyTorch?

Yes, YOLO11 can be integrated with other AI frameworks like TensorFlow and PyTorch. Ultralytics provides support for exporting YOLO11 models to various formats like ONNX, TensorRT, and CoreML, ensuring smooth interoperability with other ML frameworks.

To export a YOLO11 model to ONNX format:

yolo export --weights yolo11n.pt --include onnx

Learn more about exporting models in our guide on export.

How accurate is the speed estimation using Ultralytics YOLO11?

The accuracy of speed estimation using Ultralytics YOLO11 depends on several factors, including the quality of the object tracking, the resolution and frame rate of the video, and environmental variables. While the speed estimator provides reliable estimates, it may not be 100% accurate due to variances in frame processing speed and object occlusion.

Note: Always consider margin of error and validate the estimates with ground truth data when possible.

For further accuracy improvement tips, check the Arguments SpeedEstimator section.

Why choose Ultralytics YOLO11 over other object detection models like TensorFlow Object Detection API?

Ultralytics YOLO11 offers several advantages over other object detection models, such as the TensorFlow Object Detection API:

  • Real-Time Performance: YOLO11 is optimized for real-time detection, providing high speed and accuracy.
  • Ease of Use: Designed with a user-friendly interface, YOLO11 simplifies model training and deployment.
  • Versatility: Supports multiple tasks, including object detection, segmentation, and pose estimation.
  • Community and Support: YOLO11 is backed by an active community and extensive documentation, ensuring developers have the resources they need.

For more information on the benefits of YOLO11, explore our detailed model page.

📅 Created 11 months ago ✏️ Updated 17 days ago

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